Utah FORGE 2-2439v2: Reports on Stress Prediction and Modeling for Well 16B(78)-32 - May 2025
These two reports from the University of Pittsburgh document related efforts under Utah FORGE Project 2-2439v2 to estimate in-situ stresses in well 16B(78)-32 using laboratory data, machine learning models, and physics-based simulations. One report focuses on developing and validating stress prediction models using ultrasonic velocity experiments on core samples and applying those models to sonic log data. The other report uses those near-field predictions as input to a thermo-poro-mechanical model to estimate far-field stress profiles under various thermal and pore pressure conditions.
Citation Formats
TY - DATA
AB - These two reports from the University of Pittsburgh document related efforts under Utah FORGE Project 2-2439v2 to estimate in-situ stresses in well 16B(78)-32 using laboratory data, machine learning models, and physics-based simulations. One report focuses on developing and validating stress prediction models using ultrasonic velocity experiments on core samples and applying those models to sonic log data. The other report uses those near-field predictions as input to a thermo-poro-mechanical model to estimate far-field stress profiles under various thermal and pore pressure conditions.
AU - Lu, Guanyi
A2 - Mustafa, Ayyaz
A3 - Bunger, Andrew
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO -
KW - geothermal
KW - energy
KW - Utah
KW - 16B78-32
KW - In-Situ Stress
KW - Ultrasonic Velocity
KW - Utah FORGE
KW - EGS
KW - 16B
KW - machine learning
KW - true triaxial testing
KW - sonic logs
KW - stress prediction
KW - far-field stress
KW - thermo-poro-mechanical
KW - modeling
KW - deep learning
KW - finite element model
KW - geothermal reservoir
KW - stress profiling
KW - stress anisotropy
KW - technical report
LA - English
DA - 2025/06/05
PY - 2025
PB - University of Pittsburgh
T1 - Utah FORGE 2-2439v2: Reports on Stress Prediction and Modeling for Well 16B(78)-32 - May 2025
UR - https://data.openei.org/submissions/8431
ER -
Lu, Guanyi, et al. Utah FORGE 2-2439v2: Reports on Stress Prediction and Modeling for Well 16B(78)-32 - May 2025. University of Pittsburgh, 5 June, 2025, GDR. https://gdr.openei.org/submissions/1742.
Lu, G., Mustafa, A., & Bunger, A. (2025). Utah FORGE 2-2439v2: Reports on Stress Prediction and Modeling for Well 16B(78)-32 - May 2025. [Data set]. GDR. University of Pittsburgh. https://gdr.openei.org/submissions/1742
Lu, Guanyi, Ayyaz Mustafa, and Andrew Bunger. Utah FORGE 2-2439v2: Reports on Stress Prediction and Modeling for Well 16B(78)-32 - May 2025. University of Pittsburgh, June, 5, 2025. Distributed by GDR. https://gdr.openei.org/submissions/1742
@misc{OEDI_Dataset_8431,
title = {Utah FORGE 2-2439v2: Reports on Stress Prediction and Modeling for Well 16B(78)-32 - May 2025},
author = {Lu, Guanyi and Mustafa, Ayyaz and Bunger, Andrew},
abstractNote = {These two reports from the University of Pittsburgh document related efforts under Utah FORGE Project 2-2439v2 to estimate in-situ stresses in well 16B(78)-32 using laboratory data, machine learning models, and physics-based simulations. One report focuses on developing and validating stress prediction models using ultrasonic velocity experiments on core samples and applying those models to sonic log data. The other report uses those near-field predictions as input to a thermo-poro-mechanical model to estimate far-field stress profiles under various thermal and pore pressure conditions.},
url = {https://gdr.openei.org/submissions/1742},
year = {2025},
howpublished = {GDR, University of Pittsburgh, https://gdr.openei.org/submissions/1742},
note = {Accessed: 2025-06-09}
}
Details
Data from Jun 5, 2025
Last updated Jun 9, 2025
Submitted Jun 5, 2025
Organization
University of Pittsburgh
Contact
Andrew Bunger
412.624.9875
Authors
Original Source
https://gdr.openei.org/submissions/1742Research Areas
Keywords
geothermal, energy, Utah, 16B78-32, In-Situ Stress, Ultrasonic Velocity, Utah FORGE, EGS, 16B, machine learning, true triaxial testing, sonic logs, stress prediction, far-field stress, thermo-poro-mechanical, modeling, deep learning, finite element model, geothermal reservoir, stress profiling, stress anisotropy, technical reportDOE Project Details
Project Name Utah FORGE
Project Lead Lauren Boyd
Project Number EE0007080
